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	<title>bioRxiv Channel: Drug Development and Clinical Therapeutics </title>
	<link>https://biorxiv.org</link>
	<description>
	This feed contains articles for bioRxiv Channel "Drug Development and Clinical Therapeutics "
	</description>

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	<prism:publicationName>bioRxiv</prism:publicationName>
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	<title>bioRxiv</title>
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	<link>https://biorxiv.org</link>
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	<item rdf:about="https://biorxiv.org/cgi/content/short/123240v1?rss=1">
<title>
<![CDATA[
Practical Unidentifiability Of Receptor Density In Target Mediated Drug Disposition Models Can Lead To Over-Interpretation Of Drug Concentration Data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/123240v1?rss=1"
</link>
<description><![CDATA[
For monoclonal antibodies, mathematical models of target mediated drug disposition (TMDD) are often fit to data in order to estimate key physiological parameters of the system. These parameter estimates can then be used to support drug development by assisting with the assessment of whether the target is druggable and what the first in human dose should be. The TMDD model is almost always over-parameterized given the available data, resulting in the practical unidentifiability of some of the model parameters, including the target receptor density. In particular, when only PK data is available, the receptor density is almost always practically unidentifiable. However, because practical identifiability is not regularly assessed, incorrect interpretation of model fits to the data can be made. This issue is illustrated using two case studies from the literature.
]]></description>
<dc:creator>Stein, A.</dc:creator>
<dc:date>2017-04-02</dc:date>
<dc:identifier>doi:10.1101/123240</dc:identifier>
<dc:title><![CDATA[Practical Unidentifiability Of Receptor Density In Target Mediated Drug Disposition Models Can Lead To Over-Interpretation Of Drug Concentration Data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-04-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/150821v1?rss=1">
<title>
<![CDATA[
Costing ‘the’ MTD 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/150821v1?rss=1"
</link>
<description><![CDATA[
BackgroundAbsent adaptive, individualized dose-finding in early-phase oncology trials, subsequent registration trials risk suboptimal dosing that compromises statistical power and lowers the probability of technical success (PTS) for the investigational drug. While much methodological progress has been made toward adaptive dose-finding, and quantitative modeling of dose-response relationships, most such work continues to be organized around a concept of  the maximum tolerated dose (MTD). But a new methodology, Dose Titration Algorithm Tuning (DTAT), now holds forth the promise of individualized  MTDi dosing. Relative to such individualized dosing, current  one-size-fits-all dosing practices amount to a constraint that imposes costs on society. This paper estimates the magnitude of these costs.nnMethodsSimulated dose titration as in (Norris 2017) is extended to 1000 subjects, yielding an empirical MTDi distribution to which a gamma density is fitted. Individual-level efficacy, in terms of the probability of achieving remission, is assumed to be an Emax-type function of dose relative to MTDi, scaled (arbitrarily) to identify MTDi with the LD50 of the individuals tumor. (Thus, a criterion 50% of the population achieve remission under individualized dosing in this analysis.) Current practice is modeled such that all patients receive a first-cycle dose at  the MTD, and those for whom MTDi < MTDthe experience a  dose-limiting toxicity (DLT) that aborts subsequent cycles. Therapy thus terminated is assumed to confer no benefit. Individuals for whom MTDi[&ge;] MTDthe tolerate a full treatment course, and achieve remission with probability determined by the Emax curve evaluated at MTDthe/MTDi. A closed-form expression is obtained for the population remission rate, and maximized numerically over MTDthe as a free parameter, thus identifying the best result achievable under one-size-fits-all dosing. A sensitivity analysis is performed, using both a perturbation of the assumed Emax function, and an antipodal alternative specification.nnResultsSimulated MTDi follow a gamma distribution with shape parameter  {approx} 1.75. The population remission rate under one-size-fits-all dosing at the maximizing value of MTDthe proves to be a function of the shape parameter--and thus the coefficient of variation (CV)--of the gamma distribution of MTDi. Within a plausible range of CV(MTDi), one-size-fits-all dosing wastes approximately half of the drugs population-level efficacy. In the sensitivity analysis, sensitivity to the perturbation proves to be of second order. The alternative exposure-efficacy specification likewise leaves all results intact.nnConclusionsThe CV of MTDi determines the efficacy lost under one-size-fits-all dosing at  the MTD. Within plausible ranges for this CV, failure to individualize dosing can effectively halve a drugs value to society. In a competitive environment dominated by regulatory hurdles, this may reduce the value of shareholders investment in the drug to zero.nnEpilogueThe main result on one-size-fits-all dosing is generalized to regimens with several dose levels. Implications for the ongoing ALTA-1L trial are briefly explored; the 2 dose levels in the brigatinib arm of this trial may lend it a competitive advantage over the single-dose crizotinib arm.
]]></description>
<dc:creator>Norris, D. C.</dc:creator>
<dc:date>2017-06-16</dc:date>
<dc:identifier>doi:10.1101/150821</dc:identifier>
<dc:title><![CDATA[Costing ‘the’ MTD]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-06-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/240846v1?rss=1">
<title>
<![CDATA[
Precautionary Coherence Unravels Dose Escalation Designs 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/240846v1?rss=1"
</link>
<description><![CDATA[
BackgroundCoherence notions have a long history in statistics, as rhetorical devices that support the critical examination of statistical doctrines and practices. Within the special domain of dose-finding methodology, a widely-discussed coherence criterion has been advanced as a means to guard the conceptual integrity of formal dose-finding designs from ad hoc tinkering. This is not, however, the only possible coherence criterion relevant to dose finding. Indeed, a new coherence criterion emerges naturally when the near-universal practice of cohort-wise dose escalation is examined from a clinical perspective.nnMethodsThe practice of enrolling drug-naive patients into an escalation cohort is considered from a realistic perspective that acknowledges patients heterogeneity with respect to pharmacokinetics and pharmacodynamics. A new coherence criterion thereby emerges, requiring that an escalation dose be tried preferentially in participants who have already tolerated a lower dose, rather than in new enrollees who are drug-naive. The logical implications of this  precautionary coherence (PC) criterion are worked out in the setting of a 3+3 design. A  3+3/PC design that satisfies this criterion is described and visualized. A simulation study is performed, evaluating the long-run performance of this new design, relative to optimal 1-size-fits-all dosing.nnResultsUnder the PC criterion, the 3+3 dose-escalation design necessarily transmutes into a dose titration design. Two simple rules suffice to enable abandonment of low starting doses, and termination of escalation. The process of conducting the 3+3/PC trial itself models the application of a dose titration algorithm (DTA) that carries over readily into clinical care. The 3+3/PC trial also yields an interval-censored  dose-survival curve having a semantics that should prove familiar to oncology trialists. Simulated 3+3/PC trials yield DTAs over a median of 6 dose levels, achieving 50% improved population-level efficacy compared to optimal 1-size-fits-all dosing.nnConclusionsDose individualization can be accomplished within a trial conducted along  algorithmic lines resembling those of the inveterate 3+3 design. The dose-survival curve arising from this  3+3/PC design has semantics that should prove familiar and conceptually accessible to oncology trialists, and also seems capable of supporting more formal statistical treatments of the design. In the presence of sufficient heterogeneity in individualized optimal dosing, a 3+3/PC trial outperforms any conceivable 1-size-fits-all dose-finding design. This fact eliminates the rationale for the latter designs, and should put an end to the further development and promulgation of 1-size-fits-all dose finding.
]]></description>
<dc:creator>Norris, D. C.</dc:creator>
<dc:date>2017-12-29</dc:date>
<dc:identifier>doi:10.1101/240846</dc:identifier>
<dc:title><![CDATA[Precautionary Coherence Unravels Dose Escalation Designs]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-12-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/196089v1?rss=1">
<title>
<![CDATA[
Improving the Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology Models 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/196089v1?rss=1"
</link>
<description><![CDATA[
Quantitative systems pharmacology (QSP) models aim to describe mechanistically the pathophysiology of disease and predict the effects of therapies on that disease. For most drug development applications, it is important to predict not only the mean response to an intervention but also the distribution of responses, due to inter-patient variability. Given the necessary complexity of QSP models, and the sparsity of relevant human data, the parameters of QSP models are often not well determined. One approach to overcome these limitations is to develop alternative virtual patients (VPs) and virtual populations (Vpops), which allow for the exploration of parametric uncertainty and reproduce inter-patient variability in response to perturbation. Here we evaluated approaches to improve the efficiency of generating Vpops. We aimed to generate Vpops without sacrificing diversity of the VPs pathophysiologies and phenotypes. To do this, we built upon a previously published approach (Allen, Rieger et al. 2016) by (a) incorporating alternative optimization algorithms (genetic algorithm and Metropolis-Hastings) or alternatively (b) augmenting the optimized objective function. Each method improved the baseline algorithm by requiring significantly fewer plausible patients (precursors to VPs) to create a reasonable Vpop. #ddct #qsp
]]></description>
<dc:creator>Rieger, T. R.</dc:creator>
<dc:creator>Allen, R. J.</dc:creator>
<dc:creator>Bystricky, L.</dc:creator>
<dc:creator>Chen, Y.</dc:creator>
<dc:creator>Colopy, G.</dc:creator>
<dc:creator>Cui, Y.</dc:creator>
<dc:creator>Gonzalez, A.</dc:creator>
<dc:creator>Liu, Y.</dc:creator>
<dc:creator>White, R.</dc:creator>
<dc:creator>Everett, R.</dc:creator>
<dc:creator>Banks, H. T.</dc:creator>
<dc:creator>Musante, C. J.</dc:creator>
<dc:date>2017-09-29</dc:date>
<dc:identifier>doi:10.1101/196089</dc:identifier>
<dc:title><![CDATA[Improving the Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology Models]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-09-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/370817v1?rss=1">
<title>
<![CDATA[
Costing ‘the’ MTD … in 2-D 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/370817v1?rss=1"
</link>
<description><![CDATA[
BackgroundI have previously evaluated the efficiency of one-size-fits-all dosing for single agents in oncology (Norris 2017b). By means of a generic argument based on an Emax-type dose-response model, I showed that one-size-fits-all dosing may roughly halve a drugs value to society. Since much of the past decades  innovation in oncology dose-finding methodology has involved the development of special methods for combination therapies, a generalization of my earlier investigations to combination dosing seems called-for.nnMethodsFundamental to my earlier work was the premise that optimal dose is a characteristic of each individual patient, distributed across the population like any other physiologic characteristic such as height. I generalize that principle here to the 2-dimensional setting of combination dosing with drugs A and B, using a copula to build a bivariate joint distribution of (MTDi,A, MTDi,B) from single-agent marginal densities of MTDi,A and MTDi,B, and interpolating  toxicity isocontours in the (a, b)-plane between the respective monotherapy intercepts. Within this framework, three distinct notional toxicities are elaborated: one specific to drug A, a second specific to drug B, and a third  nonspecific toxicity clinically attributable to either drug. The dose-response model of (Norris 2017b) is also generalized to this 2-D scenario, with the addition of an interaction term to provide for a complementary effect from combination dosing. A population of 1,000 patients is simulated, and used as a basis to evaluate population-level efficacy of two pragmatic dose-finding designs: a dose-titration method that maximizes dose-intensity subject to tolerability, and the well-known POCRM method for 1-size-fits-all combination-dose finding. Hypothetical  oracular methods are also evaluated, to define theoretical upper limits of performance for individualized and 1-size-fits-all dosing respectively.nnResultsIn our simulation, pragmatic titration attains 89% efficiency relative to theoretically optimal individualized dosing, whereas POCRM attains only 55% efficiency. The passage from oracular individualized dosing to oracular 1-size-fits-all dosing incurs an efficiency loss of 33%, while the parallel passage (within the  pragmatic realm) from titration to POCRM incurs a loss of 38%.nnConclusionsIn light of the 33% figure above, the greater part of POCRMs 38% efficiency loss relative to titration appears attributable to POCRMs 1-size-fits-all nature, rather than to any pragmatic difficulties it confronts. Thus, appeals to pragmatic considerations would seem neither to justify the decision to use 1-size-fits-all dose-finding designs, nor to excuse their inefficiencies
]]></description>
<dc:creator>Norris, D. C.</dc:creator>
<dc:date>2018-07-17</dc:date>
<dc:identifier>doi:10.1101/370817</dc:identifier>
<dc:title><![CDATA[Costing ‘the’ MTD … in 2-D]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-07-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/206946v1?rss=1">
<title>
<![CDATA[
Complex versus simple models: ion-channel cardiac toxicity prediction 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/206946v1?rss=1"
</link>
<description><![CDATA[
There is growing interest in applying detailed mathematical models of the heart for ion-channel related cardiac toxicity prediction. However, a debate as to whether such complex models are required exists. Here an assessment in the predictive performance between two established cardiac models, gold-standard and cardiac safety simulator, and a simple linear model Bnet was conducted. Three ion-channel data-sets were extracted from literature. Each compound was designated a cardiac risk category based on information within CredibleMeds. The predictive performance of each model within each data-set was assessed via a leave-one-out cross validation. In two of the data-sets Bnet performed equally as well as the leading cardiac model, cardiac safety simulator, both of these outperformed the gold-standard model. In the 3rd data-set, which contained the most detailed ion-channel pharmacology, Bnet outperformed both cardiac models. These results highlight the importance of benchmarking models but also encourage the development of simple models.
]]></description>
<dc:creator>Mistry, H.</dc:creator>
<dc:date>2017-10-20</dc:date>
<dc:identifier>doi:10.1101/206946</dc:identifier>
<dc:title><![CDATA[Complex versus simple models: ion-channel cardiac toxicity prediction]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-10-20</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/176321v1?rss=1">
<title>
<![CDATA[
Population-based mechanistic modeling allows for quantitative predictions of drug responses across cell types 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/176321v1?rss=1"
</link>
<description><![CDATA[
Quantitative mismatches between human physiology and experimental models can present serious limitations for the development of effective therapeutics. We addressed this issue, in the context of cardiac electrophysiology, through mechanistic mathematical modeling combined with statistical analyses. Physiological metrics were simulated in heterogeneous populations describing cardiac myocytes from adult ventricles and those derived from induced pluripotent stem cells (iPSC-CMs). These simulated measures were used to construct a cross-cell type regression model that predicts adult myocyte drug responses from iPSC-CM behaviors. We found that quantitatively accurate predictions of responses to selective or non-selective drugs could be generated based on iPSC-CM responses and that the method can be extended to predict drug responses in diseased as well as healthy cells. This cross-cell type model can be of great value in drug development, and the approach, which can be applied to other fields, represents an important strategy for overcoming experimental model limitations.
]]></description>
<dc:creator>Gong, J. Q. X.</dc:creator>
<dc:creator>Sobie, E. A.</dc:creator>
<dc:date>2017-08-14</dc:date>
<dc:identifier>doi:10.1101/176321</dc:identifier>
<dc:title><![CDATA[Population-based mechanistic modeling allows for quantitative predictions of drug responses across cell types]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-08-14</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/568907v1?rss=1">
<title>
<![CDATA[
Urinary biomarker and histopathological evaluation of vancomycin and piperacillin-tazobactam nephrotoxicity in comparison with vancomycin in a rat model and a confirmatory cellular model 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/568907v1?rss=1"
</link>
<description><![CDATA[
IntroductionVancomycin and piperacillin tazobactam (VAN+TZP) are two of the most commonly utilized antibiotics in the hospital setting and are reported in clinical studies to increase acute kidney injury (AKI). However, no clinical study has demonstrated that synergistic toxicity occurs, only that serum creatinine (SCr) increases with VAN+TZP. The purpose of this study was to assess biologic plausibility by quantifying kidney injury between VAN, TZP, and VAN+TZP treatments using a translational rat model of AKI and rat kidney epithelial cell studies.nnMethods(i) Male Sprague-Dawley rats (n=32) received either saline, VAN 150 mg/kg/day intravenously, TZP 1400 mg/kg/day via intraperitoneal injection, or VAN+TZP. Animals were placed in metabolic cages pre-study and on drug dosing days 1-3. Urinary biomarkers and histopathology were analyzed. (ii) Cellular injury of VAN+TZP was assessed in serum-deprived rat kidney cells (NRK-52E) using an alamarBlue(R) viability assay. Cells were incubated with antibiotics VAN, TZP, cefepime, and gentamicin alone or combined with the same drugs plus VAN 1 mg/mL.nnResultsIn the VAN-treated rats, urinary KIM-1 and clusterin were increased on days 1, 2, and 3 compared to controls (P<0.001). Elevations were seen only after 3 days of treatment with VAN+TZP (P<0.001 KIM-1, P<0.05 clusterin). Histopathology was only elevated in the VAN group when compared to TZP as a control (P=0.04). Results were consistent across biomarkers and histopathology suggesting that adding TZP did not worsen VAN induced AKI and may even be protective. In NRK-52E cells, VAN alone caused moderate cell death with high doses (IC5048.76 mg/mL). TZP alone did not cause cellular death under the same conditions. VAN+TZP was not different from VAN alone in NRK-52E cells (P>0.2).nnConclusionsVAN+TZP does not cause more kidney injury than VAN alone in a rat model of VIKI or in rat kidney epithelial cells.
]]></description>
<dc:creator>Pais, G. M.</dc:creator>
<dc:creator>Liu, J.</dc:creator>
<dc:creator>Avedissian, S. N.</dc:creator>
<dc:creator>Xanthos, T.</dc:creator>
<dc:creator>Chalkias, A.</dc:creator>
<dc:creator>d'Aloja, E.</dc:creator>
<dc:creator>Locci, E.</dc:creator>
<dc:creator>Gilchrist, A.</dc:creator>
<dc:creator>Prozialeck, W. C.</dc:creator>
<dc:creator>Rhodes, N.</dc:creator>
<dc:creator>Lodise, T. P.</dc:creator>
<dc:creator>Fitzgerald, J. C.</dc:creator>
<dc:creator>Downes, K.</dc:creator>
<dc:creator>Zuppa, A.</dc:creator>
<dc:creator>Scheetz, M. H.</dc:creator>
<dc:date>2019-03-06</dc:date>
<dc:identifier>doi:10.1101/568907</dc:identifier>
<dc:title><![CDATA[Urinary biomarker and histopathological evaluation of vancomycin and piperacillin-tazobactam nephrotoxicity in comparison with vancomycin in a rat model and a confirmatory cellular model]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-03-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/432500v1?rss=1">
<title>
<![CDATA[
Guiding dose selection of monoclonal antibodies using a new parameter (AFTIR) for characterizing ligand binding systems 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/432500v1?rss=1"
</link>
<description><![CDATA[
Guiding the dose selection for monoclonal antibody oncology drugs is often done using methods for predicting the receptor occupancy of the drug in the tumor. In this manuscript, previous work on characterizing target inhibition at steady state using the AFIR metric [1] is extended to include a "target-tissue" compartment and the shedding of membrane-bound targets. A new potency metric AFTIR (Averarge Free Tissue target to Initial target ratio at steady state) is derived, and it depends on only four key quantities: the equilibrium binding constant, the fold-change in target expression at steady state after binding to drug, the biodistribution of target from circulation to target tissue, and the average drug concentration in circulation. The AFTIR metric is useful for guiding dose selection, for efficiently performing sensitivity analyses, and for building intuition for more complex target mediated drug disposition models. In particular, reducing the complex, physiological model to four key parameters needed to predict target inhibition helps to highlight specific parameters that are the most important to estimate in future experiments to guide drug development.nnGraphical AbstractnnO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=145 SRC="FIGDIR/small/432500_fig1.gif" ALT="Figure 1">nView larger version (30K):norg.highwire.dtl.DTLVardef@6895d7org.highwire.dtl.DTLVardef@4566e0org.highwire.dtl.DTLVardef@6515e8org.highwire.dtl.DTLVardef@8168ea_HPS_FORMAT_FIGEXP  M_FIG O_FLOATNOFig. 1C_FLOATNO Graphical AbstractnnC_FIG
]]></description>
<dc:creator>Ahmed, S.</dc:creator>
<dc:creator>Ellis, M.</dc:creator>
<dc:creator>Li, H.</dc:creator>
<dc:creator>Pallucchini, L.</dc:creator>
<dc:creator>Stein, A. M.</dc:creator>
<dc:date>2018-10-03</dc:date>
<dc:identifier>doi:10.1101/432500</dc:identifier>
<dc:title><![CDATA[Guiding dose selection of monoclonal antibodies using a new parameter (AFTIR) for characterizing ligand binding systems]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-10-03</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.11.28.402297v1?rss=1">
<title>
<![CDATA[
Accelerated Predictive Healthcare Analytics with Pumas, A High Performance Pharmaceutical Modeling and Simulation Platform 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.11.28.402297v1?rss=1"
</link>
<description><![CDATA[
Pharmacometric modeling establishes causal quantitative relationships between administered dose, tissue exposures, desired and undesired effects and patients risk factors. These models are employed to de-risk drug development and guide precision medicine decisions. However, pharmacometric tools have not been designed to handle todays heterogeneous big data and complex models. We set out to design a platform that facilitates domain-specific modeling and its integration with modern analytics to foster innovation and readiness in healthcare.

Pumas demonstrates estimation methodologies with dramatic performance advances. New ODE solver algorithms, such as coeficient-optimized higher order integrators and new automatic stiffness detecting algorithms which are robust to frequent discontinuities, give rise to a median 4x performance improvement across a wide range of stiff and non-stiff systems seen in pharmacometric applications. These methods combine with JIT compiler techniques, such as statically-sized optimizations and discrete sensitivity analysis via forward-mode automatic differentiation, to further enhance the accuracy and performance of the solving and parameter estimation process. We demonstrate that when all of these techniques are combined with a validated clinical trial dosing mechanism and non-compartmental analysis (NCA) suite, real applications like NLME fitting see a median 81x acceleration while retaining the same accuracy. Meanwhile in areas with less prior software optimization, like optimal experimental design, we see orders of magnitude performance enhancements over competitors. Further, Pumas combines these technical advances with several workflows that are automated and designed to boost productivity of the day-to-day user activity. Together we show a fast pharmacometric modeling framework for next-generation precision analytics.
]]></description>
<dc:creator>Rackauckas, C. V.</dc:creator>
<dc:creator>Ma, Y.</dc:creator>
<dc:creator>Noack, A.</dc:creator>
<dc:creator>Dixit, V.</dc:creator>
<dc:creator>Mogensen, P.</dc:creator>
<dc:creator>Byrne, S.</dc:creator>
<dc:creator>Maddhashiya, S.</dc:creator>
<dc:creator>Calderon, J. B. S.</dc:creator>
<dc:creator>Nyberg, J.</dc:creator>
<dc:creator>Gobburu, J.</dc:creator>
<dc:creator>Ivaturi, V.</dc:creator>
<dc:date>2020-11-30</dc:date>
<dc:identifier>doi:10.1101/2020.11.28.402297</dc:identifier>
<dc:title><![CDATA[Accelerated Predictive Healthcare Analytics with Pumas, A High Performance Pharmaceutical Modeling and Simulation Platform]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-11-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.11.05.467468v1?rss=1">
<title>
<![CDATA[
Erythropoietin and miRNA profiles during the menstrual cycle in relation to hematological and lipid biomarkers 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.11.05.467468v1?rss=1"
</link>
<description><![CDATA[
BackgroundCirculatory micro RNAs (miRNA) have been discussed as complementary diagnostic markers in cardiovascular diseases, and in anti-doping testing. MiR-144 and miR-486 have been associated with cholesterol homeostasis and hematopoiesis, respectively. In addition, they have been suggested as putative biomarkers for autologous blood transfusion and erythropoietin (EPO) doping. The aim of the present study was to assess the variability of miR-144-3p/5p, miR-486-5p/3p and EPO during the menstrual cycle. Secondary aim was to study the correlations between miRNAs, EPO and hematological parameters and lipids.

Methods13 healthy women with regular menses were followed with weekly blood sampling during two whole menstrual cycles. MiRNAs were analyzed using TaqMan and PCR followed by calculation of the relative expression for each miRNA using ddCT approach.

ResultsThere was no menstrual cycle variability in miRNAs and EPO. MiRNA-144-3p was associated with HDL-C (rs=-0.34, p=0.036) and miRNA-486-5p with Hb (rs=0.32, p=0.046). EPO concentrations correlated to lymphocytes (rs=-0.062, p=0.0002)), Hb (rs= -0.42, p=0.0091), HDL-C (rs=0.36, p=0.030) and triglycerides (rs=-0.54, p=0.0006).

ConclusionsThe results of this study may increase the understanding of how miR486-5p and miR144-3p as well as EPO correlate to hematopoietic and lipid biomarkers.
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<dc:creator>Bergstrom, H. H.</dc:creator>
<dc:creator>Heiland, C.</dc:creator>
<dc:creator>Bjorkhem-Bergman, L.</dc:creator>
<dc:creator>Ekstrom, L.</dc:creator>
<dc:date>2021-11-05</dc:date>
<dc:identifier>doi:10.1101/2021.11.05.467468</dc:identifier>
<dc:title><![CDATA[Erythropoietin and miRNA profiles during the menstrual cycle in relation to hematological and lipid biomarkers]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-11-05</prism:publicationDate>
<prism:section></prism:section>
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